This invention is directed to recognizing vascular structures in a digital medical image.
The diagnostically superior information available from data acquired from current imaging systems enables the detection of potential problems at earlier and more treatable stages. Given the vast quantity of detailed data acquirable from imaging systems, various algorithms must be developed to efficiently and accurately process image data. With the aid of computers, advances in image processing are generally performed on digital or digitized images.
Digital images are created from an array of numerical values representing a property (such as a grey scale value or magnetic field strength) associable with an anatomical location points referenced by a particular array location. The set of anatomical location points comprises the domain of the image. In 2-D digital images, or slice sections, the discrete array locations are termed pixels. Three-dimensional digital images can be constructed from stacked slice sections through various construction techniques known in the art. The 3-D images are made up of discrete volume elements, also referred to as voxels, composed of pixels from the 2-D images. The pixel or voxel properties can be processed to ascertain various properties about the anatomy of a patient associated with such pixels or voxels. Computer-aided diagnosis (“CAD”) systems play a critical role in the analysis and visualization of digital imaging data.
An important application of computed tomographic (CT) imaging systems, as well as magnetic resonance (MR) imaging and 3-D x-ray (XR) imaging systems, is to produce 3D image data sets for vascular analysis, which can include analysis of a variety of tortuous tubular structures such as airways, ducts, nerves, blood vessels, etc. Production of such 3D image data sets is particularly important for radiologists, who are called upon to provide thorough visual reports to allow assessments of stenosis or aneurysm parameters, quantify lengths, section sizes, angles, and related parameters. Information concerning, for example, the most acute stenosis on a selected vessel section, the largest aneurysm on a selected vessel section, or the tortuosity of a vessel, is commonly utilized by physicians to allow for surgical planning. For productivity reasons, as well as to reduce film costs, the 3D image data sets should be limited to only a small set of significant images.
To facilitate the obtaining of useful information for vascular analysis in an efficient manner, conventional medical imaging systems sometimes provide 3D visualization software. Such software is provided either on the imaging systems themselves or on analysis workstations, and provides a set of tools to perform length, angle or volume measurements and to visualize a volume in different ways, for example, using cross-sections, navigator or volume rendering. With respect to vascular analysis, in particular, the software can be used to obtain multiple oblique slices of a particular vessel to allow for analysis of the vessel.
Analyzing tortuous structures, such as airways, vessels, ducts or nerves is one of the major applications of medical imaging systems. This task is accomplished today by using multiple oblique slices to analyze local segments of these structures. These views provide a clear, undistorted picture of short sections from these objects but rarely encompass their full length. Curved reformation images provide synthetic views that capture the whole length of these tubular objects and are therefore well suited to this analysis task. True 3D length measurements along the axis can be obtained from these views and they are not too far from the real anatomy in many cases. Curved reformation images can be generated by sampling values along a curve at equidistant points to generate lines, and then translating this curve by a sampling vector to generate the next image line.
Therefore, it would be advantageous if new methods and apparatuses were developed for allowing medical imaging systems and related 3D visualization software to produce useful 3D imaging data sets in a more efficient, consistent, repeatable, rapid, and less operator-dependent manner. It would particularly be advantageous if such new methods and apparatuses facilitated vascular analysis, including the analysis and imaging of tubular vessels and related stenoses, aneurysms, and tortuosity. It further would be advantageous if such methods and apparatuses could be employed both during imaging and in post-processing after imaging is completed.